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Exploring Internet of Things (IoT) data streams generated by smart cities means not only transforming data into better business decisions in a timely way but also generating long-term location intelligence for developing new forms of urban governance and organization policies. This paper proposes a new architecture based on the edge-fog-cloud continuum to analyze IoT data streams for delivering data-driven insights in a smart parking scenario. [ABSTRACT FROM AUTHOR]

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms. [ABSTRACT FROM AUTHOR]

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends. [ABSTRACT FROM AUTHOR]

Emerging IoT applications with stringent requirements on latency and data processing have posed many challenges to cloud-centric platforms for Smart Cities. Recently, Fog Computing has been advocated as a promising approach to support such new applications and handle the increasing volume of IoT data and devices. The Fog Computing paradigm is characterized by a horizontal system-level architecture where devices close to end-users and IoT devices are used for processing, storage, and networking functions. Fog Computing platforms aim to facilitate the development of applications and systems for Smart Cities by providing services and abstractions designed to integrate data from IoT devices and various information systems deployed in the city. Despite the potential of the Fog Computing paradigm, the literature still lacks a broad, comprehensive overview of what has been investigated on the use of such paradigm in platforms for Smart Cities and open issues to be addressed in future research and development. In this paper, a systematic mapping study was performed and we present a comprehensive understanding of the use of the Fog Computing paradigm in Smart Cities platforms, providing an overview of the current state of research on this topic, and identifying important gaps in the existing approaches and promising research directions. [ABSTRACT FROM AUTHOR]

Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge research attention with machine learning-based seizure detection in cloud-fog assisted environments. The present paper also proposes a cloud-fog integrated smart neurocare approach, which performs a temporal analysis of raw electroencephalogram (EEG) signals using deep learning to detect the occurrence of epileptic seizures. This patient-independent approach makes use of single-channel EEG signals to achieve real-time and computationally efficient seizure detection at fog layer devices. It employs a maximum variance-based channel selection procedure to select only one channel of raw scalp EEG signals, followed by their filtering and segmentation into various short-duration temporal segments. To analyse EEG patterns, these segments are further fed to the proposed models of convolutional neural network, recurrent neural network and stacked autoencoder deep learning classifiers. The performance analysis through simulation results evidently reveals that the proposed convolutional neural network-based temporal analysis approach performs better than other approaches. It realises an optimum accuracy of 96.43%, sensitivity of 100% and specificity of 93.33% for 30s duration EEG segments of CHB-MIT dataset and achieves 100% accuracy, sensitivity and specificity values for EEG segments of Bonn dataset for 23.6s EEG segments. Thus, the proposed convolutional neural network-based approach emerges as an appropriate method for rapid and accurate detection of epileptic seizures in fog-cloud integrated environment. [ABSTRACT FROM AUTHOR]

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes. [ABSTRACT FROM AUTHOR]

The rapid proliferation and progress of Wireless Sensor Networks (WSN) and Internet of Things (IoT) has conducted to the formation of a gigantic amount of data and a growing need to multiple new services and resources. In spite of the main role of Cloud computing in solving these issues, IoT applications need more reduced latency with mobility support and location awareness. To overcome the mentioned limits, a new concept favouring the integration of Fog computing onto IoT is increasingly utilized. It is a motivating scheme that offers a timely task execution and data management at the network edge, in a distributed way, with the collaboration of nearby nodes. In this paper, we provide a complete study of the integration of Fog onto IoT. We discuss the various challenges that are facing the Fog/IoT paradigm and specify the main contributions that have been proposed to overcome these challenges. We give also an insight on the relationship between IoT/Fog integration concept, and other leading technologies. The open issues that need more investigation are highlighted in this paper to identify clearly the research gaps in the area of Fog computing integration onto IoT. [ABSTRACT FROM AUTHOR]

In this article, we present a comprehensive survey on time-sensitive applications implemented in fog computing environments. The goal is to research what applications are being implemented in fog computing architectures and how the temporal requirements of these applications are being addressed. We also carried out a comprehensive analysis of the articles surveyed and separate them into categories, according to a pattern found in them. Our research is important for the area of real-time systems since the concept of systems that respond in real time has presented various understandings and concepts. This variability of concept has been due to the growing requirements for fast data communication and processing. Therefore, we present different concepts of real-time and near real-time systems found in the literature and currently accepted by the academic-scientific community. Finally, we conduct an analytical discussion of the characteristics and proposal of articles. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT) data processing, cloud computing alone does not suffice due to latency constraints, bandwidth limitations, and privacy concerns. By introducing intermediary nodes closer to the edge of the network that offer compute services in proximity to IoT devices, fog computing can reduce network strain and high access latency to application services. While this is the only viable approach to enable efficient IoT applications, the issue of component placement among cloud and intermediary nodes in the fog adds a new dimension to system design. State‐of‐the‐art solutions to this issue rely on simulation or solving a formalized assignment problem through heuristics only, which both have their drawbacks. In this article, we present a five‐step process for designing practical fog‐based IoT applications that combines best practices, simulation, and testbed analysis to converge towards an efficient system architecture. We then apply this process in a smart factory case study. By deploying filtered options to a physical testbed, we show that each step of our process converges towards more efficient application designs. [ABSTRACT FROM AUTHOR]

Cloud computing has long been the main backbone that Internet of Things (IoT) devices rely on to accommodate their storage and analytical needs. However, the fact that cloud systems are often located quite far from the IoT devices and the emergence of delay-critical IoT applications urged the need for extending the cloud architecture to support delay-critical services. Given that fog nodes possess low resource capabilities compared to the cloud, matching the IoT services to appropriate fog nodes while guaranteeing minimal delay for IoT services and efficient resource utilization on fog nodes becomes quite challenging. In this context, the main limitation of existing approaches is addressing the scheduling problem from one side perspective, i.e., either fog nodes or IoT devices. To address this problem, we propose in this paper a multi-criteria intelligent IoT-Fog scheduling approach using game theory. Our solution consists of designing (1) preference functions for the IoT and fog layers to enable them to rank each other based on several criteria latency and resource utilization and (2) centralized and distributed intelligent scheduling algorithms that capitalize on matching theory and consider the preferences of both parties. Simulation results reveal that our solution outperforms the two common Min-Min and Max-Min scheduling approaches in terms of IoT services execution makespan and fog nodes resource consolidation efficiency. [ABSTRACT FROM AUTHOR]

In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high computing capabilities. Processing all the data in the cloud may not be sufficient in cases when we need privacy and low latency, and when we have limited Internet bandwidth, or it is simply too expensive. It poses a challenge for creating a new generation of fog computing that supports artificial intelligence and selects the architecture appropriate for an intelligent solution. In this article, we show from four perspectives, namely, hardware, software libraries, platforms, and current applications, the landscape of components used for developing intelligent Internet of Things solutions located near where the data are generated. This way, we pinpoint the odds and risks of artificial intelligence fog computing and help in the process of selecting suitable architecture and components that will satisfy all requirements defined by the complex Internet of Things systems. [ABSTRACT FROM AUTHOR]

The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, death. In this paper, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on e-health system availability and to identify potential bottlenecks. To feed our models with real data, we design and build a prototype and execute performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved. [ABSTRACT FROM AUTHOR]